This study established an intelligent identification method for adulterated peanut oil using machine learning. A total of 32 pure peanut oil samples and 126 adulterated samples containing 5%-30% soybean, palm, cottonseed, or sunflower oil were prepared. The fatty acid profiles of pure and adulterated oils were highly similar, making them difficult to distinguish effectively using principal component analysis or heatmaps. By applying four supervised machine learning algorithms (support vector machine (SVM), random forest, partial least squares, and decision tree), the classification accuracy is improved significantly. The SVM model performed best, achieving 98.18%-100% accuracy for both single and mixed adulteration samples. SHAP analysis identified lignoceric acid (C24:0) as the key adulteration marker. The regression model yielded R2 values of 0.9153 and 0.7254 on the training and test sets, respectively. This method provides an accurate, interpretable approach for identifying peanut oil adulteration.
Keywords: Adulteration; Fatty acid; Intelligent; Machine learning; Peanut oil.
© 2026 The Authors.